Machine Learning is a new trending field these days and is an application of artificial intelligence. It uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. The main aim of machine learning is to create intelligent machines which can think and work like human beings.

Requirements of creating good machine learning systems

So what is required for creating such intelligent systems? Following are the things required in creating such machine learning systems:

Iteration – The complete process is an iterative i.e. repetition of the process.

Scalability – The capacity of the machine can be increased or decreased in size and scale.

Modeling – The models are created according to the demand by the process of modeling.

Methods of Machine Learning

The methods are classified into certain categories. These are:

Supervised Learning – In this method, input and output is provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.

Unsupervised Learning – In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied on transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.

Reinforcement Learning – This type of learning uses three components namely – agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.

How does machine learning work?

Machine learning makes use of processes similar to that of data mining. The algorithms are described in terms of target function(f) that maps input variable (x) to an output variable (y). This can be represented as:

y=f(x)

There is also an error e which is the independent of the input variable x. Thus the more generalized form of the equation is:

y=f(x) + e

The common type of machine learning is to learn the mapping of x to y for predictions. This method is known as predictive modeling to make most accurate predictions. There are various assumptions for this function.

Applications of Machine Learning

Following are some of the applications:

Cognitive Services

Medical Services

Language Processing

Business Management

Image Recognition

Face Detection

Video Games

Benefits of Machine Learning

Everything is dependent on these systems. Find out what are the benefits of this.

Decision making is faster – It provides the best possible outcomes by prioritizing the routine decision-making processes.

Adaptability – It provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.

Innovation – It uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.

Insight – It helps in understanding unique data patterns and based on which specific actions can be taken.

Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.

Outcome will be good – With this the quality of the outcome will be improved with lesser chances of error.

Deep Learning

Deep Learning is a part of the broader field machine learning and is based on data representation learning. It is based on the interpretation of artificial neural network. Deep Learning algorithm uses many layers of processing. Each layer uses the output of previous layer as an input to itself. The algorithm used can be supervised algorithm or unsupervised algorithm.

Deep Neural Network

Deep Neural Network is a type of Artificial Neural Network with multiple layers which are hidden between the input layer and the output layer. This concept is known as feature hierarchy and it tends to increase the complexity and abstraction of data. This gives network the ability to handle very large, high-dimensional data sets having millions of parameters.